{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "538196c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0ff4c986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_REF</th>\n",
       "      <th>GSM5001936</th>\n",
       "      <th>GSM5001937</th>\n",
       "      <th>GSM5001938</th>\n",
       "      <th>GSM5001939</th>\n",
       "      <th>GSM5001940</th>\n",
       "      <th>GSM5001941</th>\n",
       "      <th>GSM5001942</th>\n",
       "      <th>GSM5001943</th>\n",
       "      <th>GSM5001944</th>\n",
       "      <th>...</th>\n",
       "      <th>GSM5002047</th>\n",
       "      <th>GSM5002048</th>\n",
       "      <th>GSM5002049</th>\n",
       "      <th>GSM5002050</th>\n",
       "      <th>GSM5002051</th>\n",
       "      <th>GSM5002052</th>\n",
       "      <th>GSM5002053</th>\n",
       "      <th>GSM5002054</th>\n",
       "      <th>GSM5002055</th>\n",
       "      <th>GSM5002056</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007_s_at</td>\n",
       "      <td>725.8980</td>\n",
       "      <td>796.0900</td>\n",
       "      <td>512.20100</td>\n",
       "      <td>1073.3500</td>\n",
       "      <td>911.0790</td>\n",
       "      <td>723.7810</td>\n",
       "      <td>853.90600</td>\n",
       "      <td>797.07600</td>\n",
       "      <td>447.0500</td>\n",
       "      <td>...</td>\n",
       "      <td>989.9150</td>\n",
       "      <td>771.9320</td>\n",
       "      <td>560.6720</td>\n",
       "      <td>909.0870</td>\n",
       "      <td>537.1380</td>\n",
       "      <td>950.6690</td>\n",
       "      <td>1401.660</td>\n",
       "      <td>1086.33000</td>\n",
       "      <td>1073.8700</td>\n",
       "      <td>1053.52000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1053_at</td>\n",
       "      <td>395.5860</td>\n",
       "      <td>399.6040</td>\n",
       "      <td>387.95500</td>\n",
       "      <td>370.9710</td>\n",
       "      <td>470.6210</td>\n",
       "      <td>382.3690</td>\n",
       "      <td>307.47900</td>\n",
       "      <td>355.33600</td>\n",
       "      <td>426.2140</td>\n",
       "      <td>...</td>\n",
       "      <td>468.1380</td>\n",
       "      <td>339.8340</td>\n",
       "      <td>444.1970</td>\n",
       "      <td>277.7600</td>\n",
       "      <td>433.6500</td>\n",
       "      <td>441.2880</td>\n",
       "      <td>482.165</td>\n",
       "      <td>281.64000</td>\n",
       "      <td>362.3690</td>\n",
       "      <td>459.42000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>117_at</td>\n",
       "      <td>2733.8700</td>\n",
       "      <td>2608.8000</td>\n",
       "      <td>2322.60000</td>\n",
       "      <td>2639.6300</td>\n",
       "      <td>3788.7800</td>\n",
       "      <td>4272.0200</td>\n",
       "      <td>2723.17000</td>\n",
       "      <td>2084.72000</td>\n",
       "      <td>4792.2900</td>\n",
       "      <td>...</td>\n",
       "      <td>2862.3600</td>\n",
       "      <td>2175.9300</td>\n",
       "      <td>2152.0700</td>\n",
       "      <td>1599.2100</td>\n",
       "      <td>2378.1300</td>\n",
       "      <td>1448.0500</td>\n",
       "      <td>2308.200</td>\n",
       "      <td>1598.52000</td>\n",
       "      <td>3011.1000</td>\n",
       "      <td>2247.66000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>121_at</td>\n",
       "      <td>1440.2100</td>\n",
       "      <td>1500.6300</td>\n",
       "      <td>1262.00000</td>\n",
       "      <td>1547.6700</td>\n",
       "      <td>1092.4300</td>\n",
       "      <td>1104.6600</td>\n",
       "      <td>1393.71000</td>\n",
       "      <td>948.32200</td>\n",
       "      <td>676.1600</td>\n",
       "      <td>...</td>\n",
       "      <td>1695.9300</td>\n",
       "      <td>829.6490</td>\n",
       "      <td>1411.2200</td>\n",
       "      <td>1510.9200</td>\n",
       "      <td>1765.7500</td>\n",
       "      <td>1642.1500</td>\n",
       "      <td>1986.370</td>\n",
       "      <td>1558.57000</td>\n",
       "      <td>1989.4400</td>\n",
       "      <td>2080.07000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1255_g_at</td>\n",
       "      <td>38.3712</td>\n",
       "      <td>39.1397</td>\n",
       "      <td>5.62705</td>\n",
       "      <td>10.2502</td>\n",
       "      <td>58.7812</td>\n",
       "      <td>35.7721</td>\n",
       "      <td>6.09315</td>\n",
       "      <td>4.19176</td>\n",
       "      <td>22.2372</td>\n",
       "      <td>...</td>\n",
       "      <td>82.0326</td>\n",
       "      <td>32.7375</td>\n",
       "      <td>23.0333</td>\n",
       "      <td>39.0699</td>\n",
       "      <td>18.0678</td>\n",
       "      <td>12.3742</td>\n",
       "      <td>118.104</td>\n",
       "      <td>9.95137</td>\n",
       "      <td>56.7037</td>\n",
       "      <td>9.38501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_REF  GSM5001936  GSM5001937  GSM5001938  GSM5001939  GSM5001940  \\\n",
       "0  1007_s_at    725.8980    796.0900   512.20100   1073.3500    911.0790   \n",
       "1    1053_at    395.5860    399.6040   387.95500    370.9710    470.6210   \n",
       "2     117_at   2733.8700   2608.8000  2322.60000   2639.6300   3788.7800   \n",
       "3     121_at   1440.2100   1500.6300  1262.00000   1547.6700   1092.4300   \n",
       "4  1255_g_at     38.3712     39.1397     5.62705     10.2502     58.7812   \n",
       "\n",
       "   GSM5001941  GSM5001942  GSM5001943  GSM5001944  ...  GSM5002047  \\\n",
       "0    723.7810   853.90600   797.07600    447.0500  ...    989.9150   \n",
       "1    382.3690   307.47900   355.33600    426.2140  ...    468.1380   \n",
       "2   4272.0200  2723.17000  2084.72000   4792.2900  ...   2862.3600   \n",
       "3   1104.6600  1393.71000   948.32200    676.1600  ...   1695.9300   \n",
       "4     35.7721     6.09315     4.19176     22.2372  ...     82.0326   \n",
       "\n",
       "   GSM5002048  GSM5002049  GSM5002050  GSM5002051  GSM5002052  GSM5002053  \\\n",
       "0    771.9320    560.6720    909.0870    537.1380    950.6690    1401.660   \n",
       "1    339.8340    444.1970    277.7600    433.6500    441.2880     482.165   \n",
       "2   2175.9300   2152.0700   1599.2100   2378.1300   1448.0500    2308.200   \n",
       "3    829.6490   1411.2200   1510.9200   1765.7500   1642.1500    1986.370   \n",
       "4     32.7375     23.0333     39.0699     18.0678     12.3742     118.104   \n",
       "\n",
       "   GSM5002054  GSM5002055  GSM5002056  \n",
       "0  1086.33000   1073.8700  1053.52000  \n",
       "1   281.64000    362.3690   459.42000  \n",
       "2  1598.52000   3011.1000  2247.66000  \n",
       "3  1558.57000   1989.4400  2080.07000  \n",
       "4     9.95137     56.7037     9.38501  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#데이터 불러오기\n",
    "dataset = pd.read_csv('./colon_cancer_blood_dataset.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84ed976a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.transpose()\n",
    "dataset = dataset.rename(columns=dataset.iloc[0])\n",
    "dataset.drop(dataset.index[0], inplace=True)\n",
    "dataset.drop(dataset.columns[-1], axis=1, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb33d855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1007_s_at</th>\n",
       "      <th>1053_at</th>\n",
       "      <th>117_at</th>\n",
       "      <th>121_at</th>\n",
       "      <th>1255_g_at</th>\n",
       "      <th>1294_at</th>\n",
       "      <th>1316_at</th>\n",
       "      <th>1320_at</th>\n",
       "      <th>1405_i_at</th>\n",
       "      <th>1431_at</th>\n",
       "      <th>...</th>\n",
       "      <th>AFFX-r2-Ec-bioD-3_at</th>\n",
       "      <th>AFFX-r2-Ec-bioD-5_at</th>\n",
       "      <th>AFFX-r2-P1-cre-3_at</th>\n",
       "      <th>AFFX-r2-P1-cre-5_at</th>\n",
       "      <th>AFFX-ThrX-3_at</th>\n",
       "      <th>AFFX-ThrX-5_at</th>\n",
       "      <th>AFFX-ThrX-M_at</th>\n",
       "      <th>AFFX-TrpnX-3_at</th>\n",
       "      <th>AFFX-TrpnX-5_at</th>\n",
       "      <th>AFFX-TrpnX-M_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GSM5001936</th>\n",
       "      <td>725.898</td>\n",
       "      <td>395.586</td>\n",
       "      <td>2733.87</td>\n",
       "      <td>1440.21</td>\n",
       "      <td>38.3712</td>\n",
       "      <td>1259.94</td>\n",
       "      <td>282.894</td>\n",
       "      <td>22.3207</td>\n",
       "      <td>23357.6</td>\n",
       "      <td>61.4845</td>\n",
       "      <td>...</td>\n",
       "      <td>32278.4</td>\n",
       "      <td>19026.7</td>\n",
       "      <td>83572.0</td>\n",
       "      <td>59173.0</td>\n",
       "      <td>927.437</td>\n",
       "      <td>406.163</td>\n",
       "      <td>580.921</td>\n",
       "      <td>42.1028</td>\n",
       "      <td>81.7244</td>\n",
       "      <td>9.91339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM5001937</th>\n",
       "      <td>796.09</td>\n",
       "      <td>399.604</td>\n",
       "      <td>2608.8</td>\n",
       "      <td>1500.63</td>\n",
       "      <td>39.1397</td>\n",
       "      <td>1121.5</td>\n",
       "      <td>206.749</td>\n",
       "      <td>95.3111</td>\n",
       "      <td>20629.3</td>\n",
       "      <td>100.375</td>\n",
       "      <td>...</td>\n",
       "      <td>35960.7</td>\n",
       "      <td>22217.7</td>\n",
       "      <td>94596.8</td>\n",
       "      <td>65353.5</td>\n",
       "      <td>1360.55</td>\n",
       "      <td>646.118</td>\n",
       "      <td>969.375</td>\n",
       "      <td>51.2552</td>\n",
       "      <td>16.7812</td>\n",
       "      <td>5.35075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM5001938</th>\n",
       "      <td>512.201</td>\n",
       "      <td>387.955</td>\n",
       "      <td>2322.6</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>5.62705</td>\n",
       "      <td>1126.7</td>\n",
       "      <td>347.664</td>\n",
       "      <td>12.371</td>\n",
       "      <td>27487.3</td>\n",
       "      <td>109.289</td>\n",
       "      <td>...</td>\n",
       "      <td>27478.8</td>\n",
       "      <td>17796.6</td>\n",
       "      <td>75012.9</td>\n",
       "      <td>52527.9</td>\n",
       "      <td>1149.93</td>\n",
       "      <td>623.787</td>\n",
       "      <td>938.115</td>\n",
       "      <td>36.6352</td>\n",
       "      <td>38.5536</td>\n",
       "      <td>3.54501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM5001939</th>\n",
       "      <td>1073.35</td>\n",
       "      <td>370.971</td>\n",
       "      <td>2639.63</td>\n",
       "      <td>1547.67</td>\n",
       "      <td>10.2502</td>\n",
       "      <td>1708.83</td>\n",
       "      <td>380.185</td>\n",
       "      <td>6.04854</td>\n",
       "      <td>40321.1</td>\n",
       "      <td>18.2195</td>\n",
       "      <td>...</td>\n",
       "      <td>62129.5</td>\n",
       "      <td>38590.3</td>\n",
       "      <td>187277.0</td>\n",
       "      <td>111348.0</td>\n",
       "      <td>383.647</td>\n",
       "      <td>117.674</td>\n",
       "      <td>223.904</td>\n",
       "      <td>24.8811</td>\n",
       "      <td>97.5604</td>\n",
       "      <td>11.6109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GSM5001940</th>\n",
       "      <td>911.079</td>\n",
       "      <td>470.621</td>\n",
       "      <td>3788.78</td>\n",
       "      <td>1092.43</td>\n",
       "      <td>58.7812</td>\n",
       "      <td>1173.27</td>\n",
       "      <td>206.402</td>\n",
       "      <td>34.4467</td>\n",
       "      <td>14319.7</td>\n",
       "      <td>98.1218</td>\n",
       "      <td>...</td>\n",
       "      <td>31335.8</td>\n",
       "      <td>19608.5</td>\n",
       "      <td>89518.4</td>\n",
       "      <td>60446.0</td>\n",
       "      <td>791.052</td>\n",
       "      <td>211.981</td>\n",
       "      <td>403.888</td>\n",
       "      <td>10.6119</td>\n",
       "      <td>30.3497</td>\n",
       "      <td>4.82076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54675 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           1007_s_at  1053_at   117_at   121_at 1255_g_at  1294_at  1316_at  \\\n",
       "GSM5001936   725.898  395.586  2733.87  1440.21   38.3712  1259.94  282.894   \n",
       "GSM5001937    796.09  399.604   2608.8  1500.63   39.1397   1121.5  206.749   \n",
       "GSM5001938   512.201  387.955   2322.6   1262.0   5.62705   1126.7  347.664   \n",
       "GSM5001939   1073.35  370.971  2639.63  1547.67   10.2502  1708.83  380.185   \n",
       "GSM5001940   911.079  470.621  3788.78  1092.43   58.7812  1173.27  206.402   \n",
       "\n",
       "            1320_at 1405_i_at  1431_at  ... AFFX-r2-Ec-bioD-3_at  \\\n",
       "GSM5001936  22.3207   23357.6  61.4845  ...              32278.4   \n",
       "GSM5001937  95.3111   20629.3  100.375  ...              35960.7   \n",
       "GSM5001938   12.371   27487.3  109.289  ...              27478.8   \n",
       "GSM5001939  6.04854   40321.1  18.2195  ...              62129.5   \n",
       "GSM5001940  34.4467   14319.7  98.1218  ...              31335.8   \n",
       "\n",
       "           AFFX-r2-Ec-bioD-5_at AFFX-r2-P1-cre-3_at AFFX-r2-P1-cre-5_at  \\\n",
       "GSM5001936              19026.7             83572.0             59173.0   \n",
       "GSM5001937              22217.7             94596.8             65353.5   \n",
       "GSM5001938              17796.6             75012.9             52527.9   \n",
       "GSM5001939              38590.3            187277.0            111348.0   \n",
       "GSM5001940              19608.5             89518.4             60446.0   \n",
       "\n",
       "           AFFX-ThrX-3_at AFFX-ThrX-5_at AFFX-ThrX-M_at AFFX-TrpnX-3_at  \\\n",
       "GSM5001936        927.437        406.163        580.921         42.1028   \n",
       "GSM5001937        1360.55        646.118        969.375         51.2552   \n",
       "GSM5001938        1149.93        623.787        938.115         36.6352   \n",
       "GSM5001939        383.647        117.674        223.904         24.8811   \n",
       "GSM5001940        791.052        211.981        403.888         10.6119   \n",
       "\n",
       "           AFFX-TrpnX-5_at AFFX-TrpnX-M_at  \n",
       "GSM5001936         81.7244         9.91339  \n",
       "GSM5001937         16.7812         5.35075  \n",
       "GSM5001938         38.5536         3.54501  \n",
       "GSM5001939         97.5604         11.6109  \n",
       "GSM5001940         30.3497         4.82076  \n",
       "\n",
       "[5 rows x 54675 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bc0423a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121, 54675)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f39b293b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2688df3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 121 entries, GSM5001936 to GSM5002056\n",
      "Columns: 54675 entries, 1007_s_at to AFFX-TrpnX-M_at\n",
      "dtypes: float64(54675)\n",
      "memory usage: 50.5+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e8f4b593",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            1007_s_at  1053_at   117_at    121_at  1255_g_at  1294_at  \\\n",
      "GSM5001999    449.939  313.459  3463.42   773.577    2.36404  1257.45   \n",
      "GSM5002040    739.061  323.431  2832.61  1030.020    4.56984  1726.73   \n",
      "GSM5002026    548.046  440.419  2004.09  1428.990    8.06940  1376.54   \n",
      "GSM5002042    762.723  471.940  2049.25  1330.490    8.46255  2459.26   \n",
      "GSM5002002    294.411  383.143  2911.48   704.427   45.51480  1016.80   \n",
      "GSM5001970    757.428  304.677  2176.02   915.898   12.72660  1379.29   \n",
      "GSM5001946    681.135  555.720  4722.07  1081.570   50.50960   899.71   \n",
      "GSM5002036    762.966  355.931  1375.75  2054.510  114.82000  2112.78   \n",
      "GSM5002010    639.030  332.241  2474.86  1341.310   35.42180  1025.49   \n",
      "GSM5001939   1073.350  370.971  2639.63  1547.670   10.25020  1708.83   \n",
      "\n",
      "            1316_at   1320_at  1405_i_at  1431_at  ...  AFFX-r2-Ec-bioD-5_at  \\\n",
      "GSM5001999  336.967   6.13305    23657.2  31.2548  ...               24048.9   \n",
      "GSM5002040  258.489  13.84580    37656.5  13.7871  ...               33371.7   \n",
      "GSM5002026  314.142  15.69940    30599.2  24.1936  ...               25788.4   \n",
      "GSM5002042  474.825  80.42130    43935.4  82.5702  ...               35122.0   \n",
      "GSM5002002  314.803  14.08880    25999.4  34.6803  ...               22418.8   \n",
      "GSM5001970  363.820  22.10480    27371.6  25.8362  ...               21874.8   \n",
      "GSM5001946  241.781  27.62600    11480.1  66.0747  ...               23900.7   \n",
      "GSM5002036  545.198  40.47460    33868.3  61.1825  ...               46997.3   \n",
      "GSM5002010  229.646  13.30900    11436.3  55.7224  ...               28271.1   \n",
      "GSM5001939  380.185   6.04854    40321.1  18.2195  ...               38590.3   \n",
      "\n",
      "            AFFX-r2-P1-cre-3_at  AFFX-r2-P1-cre-5_at  AFFX-ThrX-3_at  \\\n",
      "GSM5001999              94806.6              68185.8        2555.180   \n",
      "GSM5002040             124592.0              87680.4        3088.750   \n",
      "GSM5002026             112173.0              77923.1        1965.600   \n",
      "GSM5002042             117289.0              81046.2        2385.920   \n",
      "GSM5002002              77370.3              60149.7        1610.960   \n",
      "GSM5001970              92595.4              65840.5        1122.230   \n",
      "GSM5001946              96497.2              71268.3        8889.910   \n",
      "GSM5002036             155987.0             119597.0        4471.210   \n",
      "GSM5002010             114753.0              80175.4        1801.350   \n",
      "GSM5001939             187277.0             111348.0         383.647   \n",
      "\n",
      "            AFFX-ThrX-5_at  AFFX-ThrX-M_at  AFFX-TrpnX-3_at  AFFX-TrpnX-5_at  \\\n",
      "GSM5001999         933.640        1625.910         22.58190         54.67090   \n",
      "GSM5002040        1426.220        1649.590          4.98409         22.33360   \n",
      "GSM5002026        1015.900        1394.310         23.42160         41.48890   \n",
      "GSM5002042        1147.730        1573.790          3.30883         46.48380   \n",
      "GSM5002002         809.932        1221.930          2.51553         25.36320   \n",
      "GSM5001970         676.362        1027.750          2.70038          7.85547   \n",
      "GSM5001946        3512.960        5664.430          3.80643          5.59302   \n",
      "GSM5002036        1971.300        2333.600         10.28170         73.42920   \n",
      "GSM5002010        1055.330        1340.480         33.26680         63.04330   \n",
      "GSM5001939         117.674         223.904         24.88110         97.56040   \n",
      "\n",
      "            AFFX-TrpnX-M_at  label  \n",
      "GSM5001999          6.49518      1  \n",
      "GSM5002040          4.56265      1  \n",
      "GSM5002026          9.32015      1  \n",
      "GSM5002042         51.30420      1  \n",
      "GSM5002002          4.08920      1  \n",
      "GSM5001970          3.98158      0  \n",
      "GSM5001946          2.40095      0  \n",
      "GSM5002036         11.71900      1  \n",
      "GSM5002010          3.29393      1  \n",
      "GSM5001939         11.61090      0  \n",
      "\n",
      "[10 rows x 54676 columns]\n"
     ]
    }
   ],
   "source": [
    "dataset['label'] = 0\n",
    "label=dataset['label'].copy()\n",
    "label[62:] = 1\n",
    "dataset['label'] = label\n",
    "print(dataset.sample(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4f3ab6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    62\n",
       "1    59\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#각각의 label이 몇 개의 데이터로 이루어져있는지 확인\n",
    "dataset['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8644cb56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            1007_s_at  1053_at   117_at   121_at  1255_g_at  1294_at  1316_at  \\\n",
      "GSM5001936    725.898  395.586  2733.87  1440.21   38.37120  1259.94  282.894   \n",
      "GSM5001937    796.090  399.604  2608.80  1500.63   39.13970  1121.50  206.749   \n",
      "GSM5001938    512.201  387.955  2322.60  1262.00    5.62705  1126.70  347.664   \n",
      "GSM5001939   1073.350  370.971  2639.63  1547.67   10.25020  1708.83  380.185   \n",
      "GSM5001940    911.079  470.621  3788.78  1092.43   58.78120  1173.27  206.402   \n",
      "...               ...      ...      ...      ...        ...      ...      ...   \n",
      "GSM5002052    950.669  441.288  1448.05  1642.15   12.37420  2558.49  413.438   \n",
      "GSM5002053   1401.660  482.165  2308.20  1986.37  118.10400  2004.94  588.651   \n",
      "GSM5002054   1086.330  281.640  1598.52  1558.57    9.95137  1790.74  517.305   \n",
      "GSM5002055   1073.870  362.369  3011.10  1989.44   56.70370  2546.02  547.327   \n",
      "GSM5002056   1053.520  459.420  2247.66  2080.07    9.38501  1681.98  386.627   \n",
      "\n",
      "             1320_at  1405_i_at   1431_at  ...  AFFX-r2-Ec-bioD-3_at  \\\n",
      "GSM5001936  22.32070    23357.6   61.4845  ...               32278.4   \n",
      "GSM5001937  95.31110    20629.3  100.3750  ...               35960.7   \n",
      "GSM5001938  12.37100    27487.3  109.2890  ...               27478.8   \n",
      "GSM5001939   6.04854    40321.1   18.2195  ...               62129.5   \n",
      "GSM5001940  34.44670    14319.7   98.1218  ...               31335.8   \n",
      "...              ...        ...       ...  ...                   ...   \n",
      "GSM5002052   5.27167    41296.6   83.6529  ...               39817.2   \n",
      "GSM5002053  23.11680    44628.5   10.6464  ...               78408.9   \n",
      "GSM5002054  17.80240    46325.3   45.6634  ...               60379.2   \n",
      "GSM5002055  28.35650    40823.0   48.6846  ...               82236.5   \n",
      "GSM5002056   5.17318    43730.3   67.8931  ...               52712.9   \n",
      "\n",
      "            AFFX-r2-Ec-bioD-5_at  AFFX-r2-P1-cre-3_at  AFFX-r2-P1-cre-5_at  \\\n",
      "GSM5001936               19026.7              83572.0              59173.0   \n",
      "GSM5001937               22217.7              94596.8              65353.5   \n",
      "GSM5001938               17796.6              75012.9              52527.9   \n",
      "GSM5001939               38590.3             187277.0             111348.0   \n",
      "GSM5001940               19608.5              89518.4              60446.0   \n",
      "...                          ...                  ...                  ...   \n",
      "GSM5002052               29435.2              93094.6              63286.1   \n",
      "GSM5002053               58973.0             165461.0             119373.0   \n",
      "GSM5002054               42900.8             131533.0              96415.2   \n",
      "GSM5002055               60778.1             160805.0             122837.0   \n",
      "GSM5002056               36758.1             117204.0              85545.7   \n",
      "\n",
      "            AFFX-ThrX-3_at  AFFX-ThrX-5_at  AFFX-ThrX-M_at  AFFX-TrpnX-3_at  \\\n",
      "GSM5001936         927.437         406.163         580.921         42.10280   \n",
      "GSM5001937        1360.550         646.118         969.375         51.25520   \n",
      "GSM5001938        1149.930         623.787         938.115         36.63520   \n",
      "GSM5001939         383.647         117.674         223.904         24.88110   \n",
      "GSM5001940         791.052         211.981         403.888         10.61190   \n",
      "...                    ...             ...             ...              ...   \n",
      "GSM5002052        1720.670         672.671        1052.650         16.91670   \n",
      "GSM5002053        3388.480        1313.150        2095.470         42.11490   \n",
      "GSM5002054        3422.620        1262.870        1890.180          1.84256   \n",
      "GSM5002055        3718.030        1607.430        2144.100          9.86691   \n",
      "GSM5002056        2390.600        1004.100        1488.980          3.07673   \n",
      "\n",
      "            AFFX-TrpnX-5_at  AFFX-TrpnX-M_at  \n",
      "GSM5001936         81.72440          9.91339  \n",
      "GSM5001937         16.78120          5.35075  \n",
      "GSM5001938         38.55360          3.54501  \n",
      "GSM5001939         97.56040         11.61090  \n",
      "GSM5001940         30.34970          4.82076  \n",
      "...                     ...              ...  \n",
      "GSM5002052         67.95750          4.43808  \n",
      "GSM5002053          7.65215          4.35664  \n",
      "GSM5002054         46.29870          5.81728  \n",
      "GSM5002055         91.88200          9.54704  \n",
      "GSM5002056          6.82025          2.38892  \n",
      "\n",
      "[121 rows x 54675 columns]\n",
      "GSM5001936    0\n",
      "GSM5001937    0\n",
      "GSM5001938    0\n",
      "GSM5001939    0\n",
      "GSM5001940    0\n",
      "             ..\n",
      "GSM5002052    1\n",
      "GSM5002053    1\n",
      "GSM5002054    1\n",
      "GSM5002055    1\n",
      "GSM5002056    1\n",
      "Name: label, Length: 121, dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 121 entries, GSM5001936 to GSM5002056\n",
      "Columns: 54676 entries, 1007_s_at to label\n",
      "dtypes: float64(54675), int64(1)\n",
      "memory usage: 50.5+ MB\n"
     ]
    }
   ],
   "source": [
    "X = dataset.drop(['label'], axis=1)\n",
    "y = dataset['label']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2 )\n",
    "print(X)\n",
    "print(y)\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0f876607",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    49\n",
      "1    47\n",
      "Name: label, dtype: int64\n",
      "SMOTE 적용 전 학습용 피처/레이블 데이터 세트:  (96, 54675) (96,)\n",
      "SMOTE 적용 후 학습용 피처/레이블 데이터 세트:  (98, 54675) (98,)\n",
      "0    49\n",
      "1    49\n",
      "Name: label, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#SMOTE 오버샘플링 적용\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = dataset.iloc[:,:-1]\n",
    "y = dataset.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)\n",
    "print(y_train.value_counts())\n",
    "smote = SMOTE()\n",
    "X_train_over, y_train_over = smote.fit_resample(X_train, y_train)\n",
    "print('SMOTE 적용 전 학습용 피처/레이블 데이터 세트: ', X_train.shape, y_train.shape)\n",
    "print('SMOTE 적용 후 학습용 피처/레이블 데이터 세트: ', X_train_over.shape, y_train_over.shape)\n",
    "print(y_train_over.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "23d74f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "#랜덤 포레스트 예측\n",
    "rf_clf = RandomForestClassifier(n_estimators = 1000, max_depth = 100)\n",
    "rf_clf.fit(X_train_over, y_train_over)\n",
    "pred = rf_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "591c6e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_clf_eval(y_test, pred):\n",
    "    confusion = confusion_matrix(y_test, pred)\n",
    "    accuracy = accuracy_score(y_test, pred)\n",
    "    precision = precision_score(y_test, pred)\n",
    "    recall = recall_score(y_test, pred)\n",
    "    f1 = f1_score(y_test,pred)\n",
    "\n",
    "    print('오차 행렬')\n",
    "    print(confusion)\n",
    "    print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f}, F1:{3:.4f}'.format(accuracy,precision, recall,f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc611ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차 행렬\n",
      "[[13  0]\n",
      " [ 3  9]]\n",
      "정확도: 0.8800, 정밀도: 1.0000, 재현율: 0.7500, F1:0.8571\n"
     ]
    }
   ],
   "source": [
    "get_clf_eval(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bb95c605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 20 entries, 222018_at to 223265_at\n",
      "Series name: None\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "20 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 876.0+ bytes\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#feature importance 분석\n",
    "ftr_importances_values = rf_clf.feature_importances_\n",
    "ftr_importances = pd.Series(ftr_importances_values, index = X_train_over.columns)\n",
    "ftr_top20 = ftr_importances.sort_values(ascending=False)[:20]\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=ftr_top20, y=ftr_top20.index)\n",
    "plt.show()\n",
    "print(ftr_top20.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "03c30d3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_0 = dataset[dataset['label']==0]\n",
    "dataset_1 = dataset[dataset['label']==1]\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(24,10), nrows=1, ncols = 2)\n",
    "fig.tight_layout()\n",
    "sns.histplot(dataset_0['244679_at'], kde=True, ax = axs[0])\n",
    "sns.histplot(dataset_1['244679_at'], kde=True, ax = axs[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c2b2b187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62.0</td>\n",
       "      <td>600.615339</td>\n",
       "      <td>147.629593</td>\n",
       "      <td>272.515</td>\n",
       "      <td>527.23925</td>\n",
       "      <td>603.518</td>\n",
       "      <td>647.20375</td>\n",
       "      <td>1094.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59.0</td>\n",
       "      <td>395.457847</td>\n",
       "      <td>168.316452</td>\n",
       "      <td>128.894</td>\n",
       "      <td>285.50900</td>\n",
       "      <td>341.184</td>\n",
       "      <td>457.67600</td>\n",
       "      <td>935.387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count        mean         std      min        25%      50%        75%  \\\n",
       "label                                                                          \n",
       "0       62.0  600.615339  147.629593  272.515  527.23925  603.518  647.20375   \n",
       "1       59.0  395.457847  168.316452  128.894  285.50900  341.184  457.67600   \n",
       "\n",
       "            max  \n",
       "label            \n",
       "0      1094.150  \n",
       "1       935.387  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby('label')['244679_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aa7e407f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1728x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_0 = dataset[dataset['label']==0]\n",
    "dataset_1 = dataset[dataset['label']==1]\n",
    "fig, axs = plt.subplots(figsize=(24,10), nrows=1, ncols = 2)\n",
    "fig.tight_layout()\n",
    "sns.histplot(dataset_0['222018_at'], kde=True, ax = axs[0])\n",
    "sns.histplot(dataset_1['222018_at'], kde=True, ax = axs[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f248d7a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMOTE 적용 전 학습용 피처/레이블 데이터 세트:  (96, 20) (96,)\n",
      "SMOTE 적용 후 학습용 피처/레이블 데이터 세트:  (98, 20) (98,)\n",
      "1    49\n",
      "0    49\n",
      "Name: label, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jinsung\\AppData\\Local\\Temp/ipykernel_33852/1464093021.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dataset_ver2['label'] = dataset['label']\n"
     ]
    }
   ],
   "source": [
    "dataset_ver2 = dataset[ftr_top20.index]\n",
    "dataset_ver2['label'] = dataset['label']\n",
    "X = dataset_ver2.iloc[:,:-1]\n",
    "y = dataset_ver2.iloc[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "smote = SMOTE()\n",
    "X_train_over, y_train_over = smote.fit_resample(X_train, y_train)\n",
    "print('SMOTE 적용 전 학습용 피처/레이블 데이터 세트: ', X_train.shape, y_train.shape)\n",
    "print('SMOTE 적용 후 학습용 피처/레이블 데이터 세트: ', X_train_over.shape, y_train_over.shape)\n",
    "print(y_train_over.value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7fefef0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jinsung\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "오차 행렬\n",
      "[[15  0]\n",
      " [ 0 10]]\n",
      "정확도: 1.0000, 정밀도: 1.0000, 재현율: 1.0000, F1:1.0000\n"
     ]
    }
   ],
   "source": [
    "#LightGBM을 이용한 분류\n",
    "lgbm_wrapper = LGBMClassifier(n_estimators=1000, learning_rate=0.05)\n",
    "lgbm_wrapper.fit(X_train_over, y_train_over, verbose=True)\n",
    "preds = lgbm_wrapper.predict(X_test)\n",
    "get_clf_eval(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "103e8638",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5300fde",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a8b313b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "368e069f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09ed8599",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
